Wigner-Ville analysis and classification of electrocardiograms during thrombolysis

Non-stationary analysis of electrocardiograms (ECGs) using Wigner-Ville distribution is presented. Analysis was performed on subjects with acute myocardial infarction who had undergone thrombolysis, in Holter recordings of lead V1. The distinction between successfully and non-successfully thrombolysed patients was evaluated, based on time-frequency features of the Wigner-Ville transformed ECGs at the sixth hour after lysis. Characteristic parameters were extracted from time-frequency areas, and linear discriminant analysis was performed on these parameters, leading to a prediction index to distinguish the two classes. Thirteen features were found statistically significant by t-test and were used for the classification with linear modelling. Out of these features, four corresponded to frequencies lower than 25 Hz and higher than 50 Hz for, roughly, the QRS complex, five features corresponded to all the frequency bands of, roughly, the ST area, and the last four features corresponded to the T-wave. The feature-vector used in linear modelling was iteratively generated, and the iterative prediction found all 18 features significant. The iterative method resulted in better classification than that of the standard statistical procedure (3.8% error against 18.1% with the classic method). The evolution of the prediction index with time for the first 12 h was different for the successfully and non-successfully thrombolysed groups. Specifically, in the successful thrombolysis group, oscillations and variation with time were more obvious, indicating a possible difference in the dynamics of the cardiac system.     

Selecting relevant electrode positions for classification tasks based on the electro-encephalogram

The aim is to describe a general approach to determining important electrode positions when measured electro-encephalogram signals are used for classification. The approach is exemplified in the frame of the brain-computer interface, which crucially depends on the classification of different brain states. To classify two brain states, e.g. planning of movement of right and left index fingers, three different approaches are compared: classification using a physiologically motivated set of four electrodes, a set determined by principal component analysis and electrodes determined by spatial pattern analysis. Spatial pattern analysis enhances the classification rate significantly from 61.3±1.8% (with four electrodes) to 71.8±1.4%, whereas the classification rate using principal component analysis is significantly lower (65.2±1.4%). Most of the 61 electrodes used have no influence on the classification rate, so that, in future experiments, the setup can be simplified drastically to six to eight electrodes without loss of information.     

A Computer Reconstruction of the Entire Coronary Arterial Tree Based on Detailed Morphometric Data

A rigorous analysis of blood flow must be based on the branching pattern and vascular geometry of the full vascular circuit of interest. It is experimentally difficult to reconstruct the entire vascular circuit of any organ because of the enormity of the vessels. The objective of the present study was to develop a novel method for the reconstruction of the full coronary vascular tree from partial measurements. Our method includes the use of data on those parts of the tree that are measured to extrapolate the data on those parts that are missing. Specifically, a two-step approach was employed in the reconstruction of the entire coronary arterial tree down to the capillary level. Vessels > 40 μm were reconstructed from cast data while vessels < 40 μm were reconstructed from histological data. The cast data were reconstructed one-bifurcation at a time while histological data were reconstructed one-sub-tree at a time by “cutting” and “pasting” of data from measured to missing vessels. The reconstruction algorithm yielded a full arterial tree down to the first capillary bifurcation with 1.9, 2.04 and 1.15 million vessel segments for the right coronary artery (RCA), left anterior descending (LAD) and left circumflex (LCx) trees, respectively. The node-to-node connectivity along with the diameter and length of every vessel segment was determined. Once the full tree was reconstructed, we automated the assignment of order numbers, according to the diameter-defined Strahler system, to every vessel segment in the tree. Consequently, the diameters, lengths, number of vessels, segments-per-element ratio, connectivity and longitudinal matrices were determined for every order number. The present model establishes a morphological foundation for future analysis of blood flow in the coronary circulation.     

CD8+T-bet+ cells as a predominant biomarker for inclusion body myositis

Background

Myositis is a heterogeneous group of muscular auto-immune diseases with clinical and pathological criteria that allow the classification of patients into different sub-groups. Inclusion body myositis is the most frequent myositis above fifty years of age. Diagnosing inclusion body myositis requires expertise and is challenging. Little is known concerning the pathogenic mechanisms of this disease in which conventional suppressive-immune therapies are inefficacious.

股骨上端形态曲线的测量、参数化与统计分析

通过对84根完好的中国人成人股骨标本进行正位和侧位两个方向的X线摄影，得到股骨正、侧两方位的X光片。对X光片上股骨上端髓腔内侧形态进行描绘，将描绘好的图像输入计算机，由计算机进行图像预处理后提取其曲线形态数据，并将形态数据参数化，从而得到可比较的、能准确表现股骨形态的量化数据，为股骨形态的分类分析和系列型人工髋关节的参数设计打下基础。     

基于小波分解的多尺度医学图像融合技术

给出了一种基于小波分解的多尺度图像融合新方法。其基本思想是 ,先对源图像进行小波多尺度分解 ;其次 ,采用了基于区域特性量测选择的加权算子的融合规则进行小波系数融合 ;最后通过小波逆变换重构融合图像。实验结果表明 ,该融合方法十分有效 ,融合图像完好地显示了源图像各自的信息。     

优化的贴片型测量探头及其生物医学应用研究

采用时域有限差分（FDTD）法结合遗传算法（GA）优化设计了适用于浅表生物组织高频测量的宽带同轴贴片探头，并用该探头测量了人体背部1～7GHz频带内的反射特性。优化设计出的圆形同轴贴片探头具有轴对称特点，辐射近场在生物组织中透人深、反射系数的频率响应特性好。所获得的人体背部反射特性的测量结果将为背部浅表组织电特性的重建提供有利的依据。     

Presenting characteristics,comorbidities, and outcomes of patients coinfected with COVID-19 and Mycoplasma pneumoniae in the USA

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is spreading at a rapid pace, and the World Health Organization declared it as pandemic on 11 March 2020. Mycoplasma pneumoniae is an "atypical" bacterial pathogen commonly known to cause respiratory illness in humans. The coinfection from SARS-CoV-2 and mycoplasma pneumonia is rarely reported in the literature to the best of our knowledge. We present a study in which 6 of 350 patients confirmed with COVID-19 were also diagnosed with M. pneumoniae infection. In this study, we described the clinical characteristics of patients with coinfection. Common symptoms at the onset of illness included fever (six [100%] patients); five (83.3%) patients had a cough, shortness of breath, and fatigue. The other symptoms were myalgia (66.6%), gastrointestinal symptoms (33.3%-50%), and altered mental status (16.7%). The laboratory parameters include lymphopenia, elevated erythrocyte sedimentation rate, C-reactive protein, lactate dehydrogenase, interleukin-6, serum ferritin, and D-dimer in all six (100%) patients. The chest X-ray at presentation showed bilateral infiltrates in all the patients (100%). We also described electrocardiogram findings, complications, and treatment during hospitalization in detail. One patient died during the hospital course.     

Fine Mapping Functional Sites or Regions from Case-Control Data Using Haplotypes of Multiple Linked SNPs

Previously, we reported an algorithm for scanning a large number of tightly linked single nucleotide polymorphisms (SNPs) for LD mapping of functional sites or regions from a family‐based association design. In the present study, we extend our method to a case‐control design. We first use the expectation maximization (EM) algorithm to estimate haplotype frequencies of multiple linked SNPs, and follow this by constructing a contingency table statistic S for LD analysis, based on the estimated haplotype frequencies. An empirical p‐value is obtained based on the null distribution of the maximum of S (S *) from a large number (e.g., 1,000 or more) of randomized permutations. The proposed algorithm has been implemented in a computer program in which window searching for functional SNP sites can cover any number of loci without limitation, except that of computer storage. Unlike other programs for a case‐control design that always conduct tests at a fix window width, in our program after setting a maximum size of haplotype window width, for a given maximum window width all possible widths of haplotypes are utilized to find the maximum statistic S * for each locus under investigation. The sensitivity of the proposed algorithm has been examined with simulated and real genotyping datasets. Association analyses indicate that our program is powerful enough to detect most, if not all, functional SNPs simulated in the original model or identified in the original report. Moreover, the program is very flexible and can be used in either regional or genome‐wide scanning for association analysis with SNP markers.     

Nonlinearity identified by neural network models inP CO 2 control system in humans

The nonlinearity included in the P_{CO 2} control system in humans is evaluated using the degree of nonlinearity based on a difference of residuals. An autoregressive moving average (ARMA) model and neural networks (linear and nonlinear) are employed to model the system, and three types of network (Jordan, Elman and fully interconnected) are compared. As the Jordan-type linear network cannot approximate respiratory data accurately, the other two types and the ARMA model are used for the evaluation of the nonlinearity. The results of the evaluation indicate that the linear assumption for the P_{CO 2} control system is invalid for three subjects out of seven. In particular, strong nonlinearity was observed for two subjects.